The term AutoRL is used in [1]} to address automatically generating a reward function for a robot navigation task. This work defines the learned reward functions for path following and target-based tasks in robot navigation. The motivation of the proxy reward function is to handle the sparsity of the original reward which is a binary value showing whether the agent reaches its objective. More specifically, the objective of the target-based task is maximizing the probability of reaching a goal as shown in Equation (REF ).
\(G_{P2P}(s)=\mathbb {I}(||s-s_g||<d_{P2P})\) 
